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Kumamoto-HamachiDataFrame Series column row
Import the package, aka import pandas as pd
A table of data is stored as a pandas DataFrame
Each column in a DataFrame is a Series
You can do things by applying a method to a DataFrame or Series
When using a Python dictionary of lists, the dictionary keys will be used as column headers and the values in each list as columns of the DataFrame.
Each column in a DataFrame is a Series
When selecting a single column of a pandas DataFrame, the result is a pandas Series.
df = pd.DataFrame(
{
"Name": [
"Braund, Mr. Owen Harris",
"Allen, Mr. William Henry",
"Bonnell, Miss. Elizabeth",
],
"Age": [22, 35, 58],
"Sex": ["male", "male", "female"],
}
)
print(df['Age']) # debug
0 22
1 35
2 58
Name: Age, dtype: int64
A pandas Series has no column labels, as it is just a single column of a DataFrame. A Series does have row labels.
Kumamoto-HamachiHow do I read and write tabular data?
Getting data in to pandas from many different file formats or data sources is supported by read_* functions.
Exporting data out of pandas is provided by different to_*methods.
The head/tail/info methods and the dtypes attribute are convenient for a first check.
Whereas read_* functions are used to read data to pandas, the to_* methods are used to store data.
Kumamoto-HamachiHow do I select a subset of a DataFrame?
The selection returned a DataFrame with 891 rows and 2 columns. Remember, a DataFrame is 2-dimensional with both a row and column dimension.
In [11]: titanic[["Age", "Sex"]].shape
Out[11]: (891, 2)
titanic["Age"] > 35 checks for which rows the Age column has a value larger than 35:
Kumamoto-HamachiHow do I select a subset of a DataFrame?
The selection returned a DataFrame with 891 rows and 2 columns. Remember, a DataFrame is 2-dimensional with both a row and column dimension.
In [11]: titanic[["Age", "Sex"]].shape
Out[11]: (891, 2)
titanic["Age"] > 35 checks for which rows the Age column has a value larger than 35:
Kumamoto-Hamachiindex
In [1]: import pandas as pd
In [2]: a = pd.DataFrame([[1,1,1,],[2,1,2],[3,2,3]],index=["one","two","three"], columns=["a","b","c"])
loc
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